The occurrence of a fault in a system or other piece of equipment may render it unavailable for service and, instead, require it to undergo maintenance in order to correct the underlying issue which caused the fault to be generated prior to being returned to service. With respect to an aircraft, a flight deck effect (FDE) generally occurs in the event of a system or subsystem failure, or the occurrence of another fault, causes a problem with the aircraft that may affect airworthiness. Some FDEs will require immediate action to remedy the underlying issue and may require that a flight either return to its origin or divert to an airport other than the original destination. Other FDEs do not affect the flight during which the FDE occurs, but will require immediate maintenance at the destination airport, which may cause a delay or cancellation of the next flight that the aircraft was scheduled to make. Other FDEs do not affect a current flight and do not require immediate action, but do create a need for unscheduled maintenance within a few days of the occurrence of the fault.
As will be apparent, the cost associated with a cancelled, diverted or turned back flight are substantial, as are the costs associated with the delay of a flight—both in terms of direct costs and the indirect costs associated with the loss of future revenues due to diminished passenger goodwill. Additionally, unscheduled maintenance is generally substantially more expensive than scheduled maintenance, both in terms of the resources required for maintenance, such as labor, shop time, expedited shipping of parts, etc., and the costs associated with an unscheduled removal of the aircraft from service. Accordingly, techniques for predicting faults, such as FDEs, can yield considerable savings by reducing unscheduled maintenance which may otherwise be necessary if an FDE unexpectedly occurs.
Accordingly, various prognostic systems have been developed to predict the occurrence of a fault, such as by estimating the remaining useful life of a system, typically expressed as a probability density function as a function of time based upon a particular future use scenario. With respect to aircraft, one prognostic approach estimates the remaining useful life of engine parts at the time of manufacture based upon fleet statistics. Such an approach has been extended to take into account the conditions of use of a particular engine system and to estimate the remaining useful life of the system. For example, an engine deployed in very hot climates will generally experience accelerated wear and a reduction in the remaining useful life relative to an engine used in more moderate climates.
Materials-based prognosis systems have also been developed to provide an estimate of the remaining useful life from the moment a system is manufactured. Materials-based systems utilize information regarding usage conditions, such as temperature, stress, etc. to estimate the remaining useful life of an individual engine part, such as a turbine blade. While materials-based prognostic systems are generally more precise than those systems premised upon fleet statistics, materials-based prognostic systems can be more costly to develop and may have a narrower range of applicability such that changes in geometry or alloy of the monitored system may significantly alter the accuracy of the predictions.
Additionally, data driven prognostic systems have been developed. Some data driven prognostic systems estimate the remaining useful life of new or deteriorated, but generally healthy, i.e., unfaulted, systems, while other data driven prognostic systems, e.g., bearings prognostic systems, estimate the remaining useful life of a system only in the presence of a fault. Ideally, data driven prognostic systems are trained on run-to-failure data from a real system; although data driven prognostic systems can be trained, with generally diminished accuracy, on data from a model of the system (e.g., a physics based model, such as a component level model of an aircraft engine). Data driven prognostic models are generally less precise than materials-based prognostic models, although both types of models are generally more precise than models based upon fleet statistics. However, data driven models are generally substantially less expensive to develop than materials-based models and may be applicable to a wider variety of systems.
With regard to data driven prognostic systems, some data driven prognostic systems utilize parametric sensor data to predict an impending fault. With respect to aircraft, parametric data may include both raw sensor measurements from the engine or airframe as well as sensor readings that have been corrected to account for flight conditions, such as altitude, ambient temperature, etc. In conjunction with an aircraft engine, for example, the parametric data may include the exhaust gas temperature, fuel flow, engine oil pressure and engine core speed. While parametric data may be useful to predict an impending fault, parametric data can be voluminous and relatively inefficient to compress such that commercial aircraft generally preserve only a few snapshots of parametric data at different intervals during a flight, such as takeoff, cruise and descent.
Other data driven prognostic systems have relied upon non-parametric data, such as the data generated in response to built-in tests that produce error log messages. For example, non-parametric error logs can be maintained which indicate when parametric measurements are beyond predefined thresholds, when certain demanded actuator positions are not reached or are not reached within a predefined time or, more generally, when a certain subsystem behaves outside of predefined operating parameters. The resulting non-parametric error logs are a collection of binary flags which are much easier to compress than parametric data and which may provide insight into the system status over an entire operational cycle, such as over an entire flight, as opposed to only at certain intervals.
With respect to aircraft, the parametric data and non-parametric data have typically been evaluated independent of one another. While such independent evaluation provides some useful information in regard to the prediction of faults within a system, the evaluation of each type of data may sometimes be limited. As such, a technique for predicting faults within an aircraft engine has been proposed in which both parametric data and non-parametric data are combined. In this regard, the non-parametric data may be transformed into parametric data in a variety of manners including message decaying and cumulative index techniques as described by Neil Eklund, et al., “A Data Fusion Approach for Aircraft Engine Fault Diagnostics,” Proceedings of ASME Turbo Expo 2007, GT2007-27941 (May 2007). The transformed non-parametric data may then be integrated with the parametric data for analysis by traditional methods. As such, the resulting diagnostic model can have the benefit of both the parametric and non-parametric data which may be beneficial to the prediction of impending faults in a reliable manner with fewer false alarms than if either the parametric or nonparametric data were considered alone.
While the combination of the parametric data and the non-parametric data may provide improvements in regard to the prediction of an impending fault, the resulting output merely indicates the likelihood, or not, of the occurrence of a fault. If the prediction occurs sufficiently in advance of the likely occurrence of the fault, maintenance can be scheduled in an economic and efficient manner, ensuring the availability of the resources required for the repair, such as parts, mechanics and service bays. However, if the fault is imminent, e.g., a fault which will likely occur during any one of the next few operational sequences, such as the next few flights, the aircraft is generally removed from service such that the cost associated with cancelled or delayed flights may still be incurred even though the prognostic system predicted the occurrence of a fault. As such, it would be desirable to not only predict the fault, but to avoid the costs and scheduling disruptions associated with cancelled or delayed flights which may otherwise occur in response to the prediction of an imminent fault by conventional prognostic systems.